U.S. Beef Industry
Erdal Erol
2022-04-14
Synopsis
Packages Required
This project aims to provide facts and figures of U.S. beef industry with R data wrangling, visualization and mapping functions and techniques.
- The most common functions in tidyverse, data.table, ggplot2, and usmap packages are employed.
- The packages knitr and DT are used to provide customized plots, images and tables for R markdown output.
- Animated plots and maps are produced with the packages gganimate, gifski and png.
#install.packages("...")
#data wrangling
library(tidyverse)
library(dtplyr)
library(tidyfast)
library(data.table)
#plots
library(ggplot2)
library(ggrepel)
library(cowplot)
#R interface/JavaScript-DataTables
library(knitr)
library(DT)
#load excel files
library(readxl)
#animated plots
library(png)
library(gifski)
library(gganimate)
#mapping
library(usmap)
#rmarkdown
library(knitr)
#USDA data with API
library(tidyUSDA)#plot theme settings
plot_theme = theme(
plot.title = element_text(face="bold", size = 20),
axis.title.x = element_text(face="bold", size = 16),
axis.text.x = element_text(face="bold",size = 14),
axis.title.y = element_text(face="bold",size = 16),
axis.text.y = element_text(face="bold",size = 14),
strip.text.x = element_text(size=rel(3.5)),
strip.text.y = element_text(size=rel(3.5)),
legend.title = element_text(face="bold",size=16),
legend.text = element_text(face="bold",size=14),
)1. Introduction
United States is the largest producer and consumer of beef in the world with 20% of the global beef production. The main stages of beef-cattle production process in U.S. are cow-calf operations, stocker/backgrounding operations, feedlots, meat packers and processors, and retailers. The production process begins with the cow-calf farms where cows and calves are raised. The next stage of production occurs at stocker/backgrounding operations where calves are placed on grass or other type of roughage. Feedlots are the final chain in the cattle production and they feed cattle with different rations of grain, silage, and/or protein supplements and sell to beef packers and processors where beef and beef by-products are produced and sold to retailers. The economic size of beef-cattle industry including direct and indirect economic contributions during on-farm and post-farm activities is estimated as $167 billion in 2016. [Source]
This project presents facts and figures of the industry with 4 main variables: production, international trade, price, and consumption. Production, international trade, and price sections include both cattle and beef sectors’ data. Consumption figures are per capita values and beef is compared with pork and chicken per capita consumption.
2. Data
2.1. Data Sources
USDA National Agricultural Statistics Service database is the data source of the project, unless stated otherwise.
2.2. Data Import
All data used in the project is provided at github repository of the project.
2.2.1. Production
Cattle
Beef cow inventory data is used for cattle production section. National, state and county level data is obtained for years available in the database.
- U.S. Beef Cattle Inventory: 1920-2022, January 1 Inventory Value of the Year.
cattleinv_data_yearly = read_csv("data/beef_cattle_inv_yearly.csv")- U.S. Beef Cattle Inventory by States: 1920-2022, January 1 Inventory Value of the Year.
cattleinv_data_state_year = read_csv("data/beef_cattle_inv_state_year.csv")- U.S. Beef Cattle Inventory by Counties: 2021, January 1.
cattleinv_data_county = read_csv("data/beef_cattle_inv_county_2021.csv")Beef
Beef production values are yearly production amount (million pounds).
- U.S. Beef Production: 1970-2021.
beef_prod_data = read_excel("data/yearly beef production.xlsx")2.2.2. International Trade
Trade data is in quantities by partner countries across years.
Cattle
Export
- U.S. Cattle Exports: 1989-2021.
export_data_cattle = read_excel("data/cattle_trade.xlsx", sheet = "export")Import
- U.S. Cattle Imports: 1989-2021.
import_data_cattle = read_excel("data/cattle_trade.xlsx", sheet = "import")Beef
Export
- U.S. Beef Exports: 1989-2021.
export_data <- read_excel("data/beef_trade.xlsx", sheet = "export")Import
- U.S. Beef Imports: 1989-2021.
import_data <- read_excel("data/beef_trade.xlsx", sheet = "import")2.2.3. Prices
Cattle
Cattle prices are monthly ‘prices received’ values for beef cattle, calves, cows and steers and heifers. All prices are dollars per cwt (hundred pounds of weight).
- U.S. Cattle Prices: 2012-2021.
cattle_price_data = read_excel("data/feedercattle_prices.xlsx")Beef
Beef price data covers monthly farm, wholesale, and retail prices. All prices are in cents per pound.
- U.S. Beef Prices: 1970-2021.
beef_price_data = read_excel("data/beef_prices.xlsx")2.2.4. Consumption
Consumption data is in per capita values.
- U.S. Meat Consumption: 1909-2019.
beef_consmptn_data = read_excel("data/beef_consumption.xls")3. Beef Industry
3.1. Production
3.1.1. Cattle
National Level
Table 1 shows U.S. beef cattle inventory. There were 30.1 million head of beef cows in U.S. as of January 1, 2022. The number is 2.3% below from 2021. It has been decreasing since 2019.
table_data = as.data.table(cattleinv_data_yearly)
table_data %>%
.[, Value:=Value/1000]%>%
.[, pct_change := (Value/lead(Value) - 1)]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 1: U.S. Beef Cattle Inventory: 1920-2022')
), colnames = c('Year', '# of Heads-1000', 'Annual Change'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits = 0)%>%
formatPercentage('pct_change', 1)The maximum value was 45.7 million on January 1, 1975 and the lowest number was 8.9 million on January 1, 1928 (Sort and Search options can be used to filter table and sort data.).
As we see from Figure 1 (a), beef cattle inventory has a decreasing trend since 1975 [Source: An Article by Kenny Burdine (University of Kentucky) ].
table_data[,pct_change := (Value/lead(Value) - 1)*100]
table_data = as.data.frame(table_data)
g1 = ggplot(table_data, aes(x = Year, y = Value)) +
labs(title = "Figure 1-a) U.S. Beef Cattle Inventory-1000 heads") +
geom_line(color="orange") +
theme_bw()+
plot_theme+
annotate(
geom = "curve", x = 1965, y = 43500, xend = 1975, yend = 45500,
curvature = -.3, arrow = arrow(length = unit(3, "mm")) )+
annotate("text", y = 43000, x = 1950, label = "max: ~45.7 milion in 1975", size =5, angle = 0)
g2 = ggplot(table_data, aes(x = Year, y = `pct_change`)) +
labs(title = "Figure 1-b) U.S. Beef Cattle Inventory-% change") +
geom_line(color="orange") +
theme_bw()+
plot_theme
plot_grid(g1, g2)
State Level
Table 2 provides inventory data for 50 states. We can check for each state’s inventory number by filtering and sorting data.
table_data = as.data.table(cattleinv_data_state_year)
table_data %>%
.[order(-Year)]%>%
.[, Value:=Value/1000]%>%
.[, c("Year", "State","Value")] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 2: U.S. Beef Cattle Inventory by State: 1920-2022')
), colnames = c('Year', 'State', 'Value-1000 heads'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits=0)The output of below code chunk tells that Texas has been the state with the highest beef cattle inventory since 1919.
table_data = as.data.table(cattleinv_data_state_year)
table_data = table_data %>%
.[, c("Year", "State", "Value")] %>%
.[order(-Value)]%>%
.[, Value:=Value/1000]%>%
.[Year==2022] %>%
.[, state_share := (Value*100/sum(Value))]%>%
.[, c("Year","State","Value", "state_share")]
distinct(table_data[table_data[, .I[Value == max(Value)], by=Year]$V1], State)Source: local data table [1 x 1]
Call: unique(`_DT1`[, .(State)])
State
<chr>
1 TEXAS
# Use as.data.table()/as.data.frame()/as_tibble() to access results
On January 1, 2022, Texas had 4.48 million beef cows. Its share was 14.9%.
The other states with the highest number of beef cows are Oklahoma, Missouri, Nebraska, and South Dakota.
Seven states have more than 1 million beef cows in 2022 with a total share of 48.83%.
table_data[,sum(state_share[1:7])][][1] 48.82971
table_data %>%
.[, c("State", "Value", "state_share")] %>%
.[, state_share := state_share/100]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 3: U.S. Beef Cattle Inventory by State: 2022')
), colnames = c('State', '# of Heads-1000', 'State Share'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits = 0)%>%
formatPercentage('state_share', 1)Figure 2 provides a map of U.S. states with their beef cow inventory on January 1, 2022.
state_map_data =table_data %>%
.[, c("State", "Value")] %>%
.[order(-Value)]
setnames(state_map_data,"State","state")
g1 =
plot_usmap(data = state_map_data, values = "Value", labels = TRUE, color = "black") +
scale_fill_continuous(name = "Beef Cattle Inventory", low = "white", high = "red", label = scales::comma) +
labs(title = "Figure 2: U.S. Beef Cattle Inventory by State, 2022-1000 heads") +
theme(plot.title = element_text(face="bold",size=20), legend.position = "right",
legend.title = element_text(face="bold",size=15), legend.text = element_text(size=12))
# Set label font size
g1$layers[[2]]$aes_params$size =5
g1
Figure 3 is an animated map of U.S. states that shows the change between 1972-2022.
table_data = as.data.table(cattleinv_data_state_year)
animated_map_data = table_data %>%
.[, c("Year", "State", "Value")] %>%
.[, Value:=Value/1000]%>%
.[Year> 1972]
setnames(animated_map_data, "State", "state")
# plot =
# plot_usmap(data = animated_map_data, values = "Value", labels = FALSE) +
# scale_fill_continuous(name = "Beef Cattle Inventory", low = "white", high = "red", label = scales::comma) +
# labs(title = "Kentucky Beef Cattle Inventory by County, 2021-heads",
# subtitle = 'Year: {frame}')+
# theme(plot.title = element_text(face="bold",size=20), legend.position = "right",
# legend.title = element_text(face="bold",size=15), legend.text = element_text(size=12))+
# transition_states(Year)
#
# animate(plot, fps = 5)County Level
Table 4 gives beef inventory across counties on January 1, 2021. Holt County in Nebraska has the highest number of beef cows.It is followed by Okeechobee-Florida, Lincoln-Nebraska, Meade-South Dakota, and Fergus-Montana.
You are welcomed to search your county’s rank and numbers using Search filter.
table_data = as.data.table(cattleinv_data_county)
table_data %>%
.[order(-Value)] %>%
.[, c("State", "County","Value")] %>%
.[, county_share := (Value/sum(Value, na.rm=TRUE))]%>%
.[, c("State", "County","Value", "county_share")] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 4: U.S. Beef Cattle Inventory by County- January 1, 2021')
), colnames = c('State','County','# of Heads', 'County Share'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits = 0)%>%
formatPercentage('county_share', 2)Kentucky
Please enjoy John Prine’s My Old Kentucky Home while reading this section!
table_data = as.data.table(cattleinv_data_state_year)
table_data %>%
.[, c("Year", "State", "Value")] %>%
.[, Value:=Value/1000]%>%
.[State=="KENTUCKY"] %>%
.[, c("Year","Value")] %>%
.[, pct_change := (Value/lead(Value) - 1)]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 3: Kentucky Beef Cattle Inventory by State: 1920-2022')
), colnames = c('Year', '# of Heads-1000', 'Annual Change'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits = 0)%>%
formatPercentage('pct_change', 1)This project presents a macro picture of the industry with 4 main variables: production, international trade, price, and consumption. Production, international trade, and price sections include both cattle and beef sectors’ data. Consumption figures are per capita values and beef is compared with pork and chicken per capita consumption.
table_data = table_data %>%
.[, Value:=Value/1000]%>%
.[State=="KENTUCKY"] %>%
.[, c("Year","Value")]
table_data[, pct_change := (Value/lead(Value) - 1)*100]
table_data = as.data.frame(table_data)
g1 = ggplot(table_data, aes(x = Year, y = Value)) +
labs(title = "a) Kentucky Cattle Inventory-1000 heads") +
geom_line(color="orange") +
theme_bw()+
plot_theme+
annotate(
geom = "curve", x = 1966, y = 1355, xend = 1975, yend = 1429,
curvature = -.3, arrow = arrow(length = unit(3, "mm")) )+
annotate("text", y = 1350, x = 1955, label = "max: ~1.5 milion in 1975", size =5, angle = 0)
g2 = ggplot(table_data, aes(x = Year, y = `pct_change`)) +
labs(title = "b) Kentucky Beef Cattle Inventory-% change") +
geom_line(color="orange") +
theme_bw()+
plot_theme
plot_grid(g1, g2)
County Level
table_data = as.data.table(cattleinv_data_county)
table_data %>%
.[order(-Value)] %>%
.[State=="KENTUCKY"] %>%
.[, c("County","Value")] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 5: Kentucky Beef Cattle Inventory by County- January 1, 2021')
), colnames = c('County','# of Heads'))%>%
formatCurrency('Value',currency = "", interval = 3, mark = ",", digits = 0)ky_data = table_data %>%
.[order(-Value)] %>%
.[State=="KENTUCKY"] %>%
.[, c("County","Value")]
setnames(ky_data,"County","county")
ky_fips = read_csv("data/fips_ky.csv")
ky_data_plot = merge(ky_data,ky_fips)
g1 =
plot_usmap(data = ky_data_plot, values = "Value", regions="county",
include = c("KY"), labels = TRUE, color = "black") +
scale_fill_continuous(name = "Beef Cattle Inventory",
low = "white", high = "cornflowerblue", label = scales::comma) +
labs(title = "Kentucky Beef Cattle Inventory by County, 2021-heads") +
theme(plot.title = element_text(face="bold",size=20), legend.position = "right",
legend.title = element_text(face="bold",size=15), legend.text = element_text(size=12))
# Set label font size
g1$layers[[2]]$aes_params$size =3
g1
3.1.2. Beef
table_data = as.data.table(beef_prod_data[, -c(3:10), with=FALSE])
table_data %>%
.[order(-year)]%>%
.[, pct_change := (production/lead(production) - 1)]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 6: U.S. Beef Production: 1970-2021')
), colnames = c('Year', 'Million pounds', 'Annual Change'))%>%
formatCurrency('production',currency = "", interval = 3, mark = ",", digits = 0)%>%
formatPercentage('pct_change', 1)This project presents a macro picture of the industry with 4 main variables: production, international trade, price, and consumption. Production, international trade, and price sections include both cattle and beef sectors’ data. Consumption figures are per capita values and beef is compared with pork and chicken per capita consumption.
table_data =table_data[order(-year)]
table_data[, pct_change := (production/lead(production) - 1)*100]
g1 = ggplot(table_data, aes(x=year,y =production)) +
labs(title ="a) U.S. Beef Production-Million pounds")+
geom_line(color="orange")+
theme_bw()+
plot_theme
g2 = ggplot(table_data, aes(x=year, y=pct_change))+
labs(title ="b) U.S. Beef Production-% Annual Change")+
geom_line(color="orange")+
theme_bw()+
plot_theme
plot_grid(g1, g2)
This project presents a macro picture of the industry with 4 main variables: production, international trade, price, and consumption. Production, international trade, and price sections include both cattle and beef sectors’ data. Consumption figures are per capita values and beef is compared with pork and chicken per capita consumption.
3.2. International Trade
3.2.1. Cattle
Export
export_data_cattle = melt(export_data_cattle, id.vars ="country",
variable.name = "year", value.name = "export")
export_data_cattle = as.data.table(export_data_cattle, key="country")
export_data_cattle[, export:=export/1000]table_data = as.data.table(export_data_cattle)
table_data %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 1: U.S. Beef Cattle Exports: 1989-2021')
), colnames = c('Country', '# Year', 'Value-'))%>%
formatCurrency('export',currency = "", interval = 3, mark = ",", digits = 0)cattle_exports = export_data_cattle[year =="2021"]
setorderv(cattle_exports, cols="export", order=-1L, na.last=TRUE)data= cattle_exports[2:111]
data= data[, c("year"):=NULL]
data[, export:=round(export*100/509.576, digits = 1)]
setnames(data, "export", "export_share")
rest_total = sum(data$export[6:111], na.rm = T)
rest_totw <- data.frame("restoftheworld", rest_total)
names(rest_totw) = c("country", "export_share")
data= data[1:5]
data1 = rbind(data, rest_totw)
table_data = setorderv(data1, cols="export_share", order=-1L, na.last=TRUE)
knitr::kable(
table_data[1:6],
col.names = c('Country', 'Share-%'),
align = "lccrr",
caption = "Table 7: U.S. Cattle Exports by Country"
)| Country | Share-% |
|---|---|
| Canada | 77.1 |
| Mexico | 18.1 |
| Pakistan | 1.8 |
| Vietnam | 1.4 |
| Turkey | 0.8 |
| restoftheworld | 0.6 |
#labels
data2 <- data1 %>%
mutate(csum = rev(cumsum(rev(export_share))),
pos = export_share/2 + lead(csum, 1),
pos = if_else(is.na(pos), export_share/2, pos))
data2 = as.data.frame(data2)
ggplot(data1, aes(x = "" , y = export_share, fill=fct_inorder(country))) +
labs(title = "U.S. Cattle Exports by Country")+
geom_col(width = 1, color = 1) +
coord_polar(theta = "y") +
scale_fill_brewer(palette = "Pastel1") +
geom_label_repel(data = data2,
aes(y = pos, label = paste0(export_share, "%")),
size = 4.5, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Country")) +
theme_bw()+
plot_theme
Import
import_data_cattle = melt(import_data_cattle, id.vars ="country",
variable.name = "year", value.name = "import")
import_data_cattle = as.data.table(import_data_cattle, key = "country")
import_data_cattle[, import:=import/1000]table_data = as.data.table(import_data_cattle)
table_data %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 1: U.S. Beef Cattle Imports: 1989-2021')
), colnames = c('Country', '# Year', 'Value-'))%>%
formatCurrency('import',currency = "", interval = 3, mark = ",", digits = 0)cattle_imports = import_data_cattle[year =="2021" ]
setorderv(cattle_imports, cols="import", order=-1L, na.last=TRUE)data= cattle_imports[2:24]
data= data[, c("year"):=NULL]
data[, import:=round(import*100/1775.492, digits = 1)]
setnames(data, "import", "import_share")
rest_total = sum(data$import[3:24], na.rm = T)
rest_totw <- data.frame("restoftheworld", rest_total)
names(rest_totw) = c("country", "import_share")
data= data[1:2]
data1 = rbind(data, rest_totw)
table_data = setorderv(data1, cols="import_share", order=-1L, na.last=TRUE)
knitr::kable(
table_data[1:3],
col.names = c('Country', 'Share-%'),
align = "lccrr",
caption = "Table 8: U.S. Cattle Imports by Country"
)| Country | Share-% |
|---|---|
| Mexico | 63.6 |
| Canada | 36.4 |
| restoftheworld | 0.0 |
#labels
data2 <- data1 %>%
mutate(csum = rev(cumsum(rev(import_share))),
pos = import_share/2 + lead(csum, 1),
pos = if_else(is.na(pos), import_share/2, pos))
data2 = as.data.frame(data2)
ggplot(data1, aes(x = "" , y = import_share, fill=fct_inorder(country))) +
labs(title = "U.S. Cattle Imports by Country")+
geom_col(width = 1, color = 1) +
coord_polar(theta = "y") +
scale_fill_brewer(palette = "Pastel1") +
geom_label_repel(data = data2,
aes(y = pos, label = paste0(import_share, "%")),
size = 4.5, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Country")) +
theme_bw()+
plot_theme
Trade Balance
cattle_trade_data = merge( export_data_cattle,import_data_cattle, by = c("country", "year"), all=T)
cattle_trade_data = cattle_trade_data[, trade_balance:= export-import]
cattle_trade_data = cattle_trade_data[, us_status:= ifelse(trade_balance<0, "net_importer", "net_exporter")]
total_cattle_trade = cattle_trade_data[country=="Total"]
setorder(total_cattle_trade, cols = -"year")
table_data = as.data.table(total_cattle_trade)
table_data %>%
.[, country:= NULL]%>%
.[, trade_balance:=round(trade_balance, digits = 0)]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 9: U.S. Cattle Trade-1000 heads')
), colnames = c('Year', 'Export', 'Import', 'Balance', 'Position'))%>%
formatCurrency(c('export','import', 'trade_balance'), currency = "", interval = 3, mark = ",", digits=0)3.2.2. Beef
dt =
beef_prod_data[, -c(3,5,7,8,9,10), with=FALSE] %>%
as.data.frame(dt) %>%
select(year, import, export) %>%
gather(key = "variable", value = "value", -year)
ggplot(dt, aes(x = year, y = value)) +
labs(title = "U.S. Beef Trade-1000 lbs") +
geom_line(aes(color = variable), size = 1) +
scale_color_manual(values = c("orange", "lightblue")) +
theme_bw()+
plot_theme
Export
export_data = melt(export_data, id.vars ="country",
variable.name = "year", value.name = "export")
export_data = as.data.table(export_data, key="country")
export_data[, export := export/1000]beef_exports = export_data[year =="2021"]
setorderv(beef_exports, cols="export", order=-1L, na.last=TRUE)data= beef_exports[2:217]
data= data[, c("year"):=NULL]
data[, export:=round(export*100/3447.0693 , digits = 1)]
setnames(data, "export", "export_share")
rest_total = sum(data$export[11:217], na.rm = T)
rest_totw <- data.frame("restoftheworld", rest_total)
names(rest_totw) = c("country", "export_share")
data= data[1:10]
data1 = rbind(data, rest_totw)
table_data = setorderv(data1, cols="export_share", order=-1L, na.last=TRUE)
knitr::kable(
table_data[1:11],
col.names = c('Country', 'Share-%'),
align = "lccrr",
caption = "Table 10:U.S. Beef Exports by Country"
)| Country | Share-% |
|---|---|
| Japan | 24.0 |
| South Korea | 22.8 |
| China | 15.7 |
| Mexico | 9.2 |
| Canada | 8.1 |
| restoftheworld | 7.4 |
| Taiwan | 5.7 |
| Hong Kong | 3.6 |
| Indonesia | 1.3 |
| Philippines | 1.0 |
| Netherlands | 0.8 |
#labels
data2 <- data1 %>%
mutate(csum = rev(cumsum(rev(export_share))),
pos = export_share/2 + lead(csum, 1),
pos = if_else(is.na(pos), export_share/2, pos))
data2 = as.data.frame(data2)
ggplot(data1, aes(x = "" , y = export_share, fill=fct_inorder(country))) +
labs(title = "U.S. Beef Exports Country Shares - %")+
geom_col(width = 1, color = 1) +
coord_polar(theta = "y") +
scale_fill_brewer(palette = "Pastel1") +
geom_label_repel(data = data2,
aes(y = pos, label = paste0(export_share, "%")),
size = 4.5, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Country")) +
theme_bw()+
plot_theme
Import
import_data = melt(import_data, id.vars ="country",
variable.name = "year", value.name = "import")
import_data = as.data.table(import_data, key = "country")
import_data[, import:=import/1000]beef_imports = import_data[year =="2021"]
setorderv(beef_imports, cols="import", order=-1L, na.last=TRUE)data= beef_imports[2:94]
data= data[, c("year"):=NULL]
data[, import:=round(import*100/3347.51555 , digits = 1)]
setnames(data, "import", "import_share")
rest_total = sum(data$import[11:217], na.rm = T)
rest_totw <- data.frame("restoftheworld", rest_total)
names(rest_totw) = c("country", "import_share")
data= data[1:10]
data1 = rbind(data, rest_totw)
table_data = setorderv(data1, cols="import_share", order=-1L, na.last=TRUE)
knitr::kable(
table_data[1:11],
col.names = c('Country', 'Share-%'),
align = "lccrr",
caption = "Table 11: U.S. Beef Imports by Country"
)| Country | Share-% |
|---|---|
| Canada | 28.1 |
| Mexico | 20.2 |
| New Zealand | 15.0 |
| Australia | 12.3 |
| Brazil | 11.0 |
| Nicaragua | 5.8 |
| Uruguay | 4.0 |
| Argentina | 1.9 |
| Costa Rica | 0.7 |
| Ireland | 0.6 |
| restoftheworld | 0.4 |
#labels
data2 <- data1 %>%
mutate(csum = rev(cumsum(rev(import_share))),
pos = import_share/2 + lead(csum, 1),
pos = if_else(is.na(pos), import_share/2, pos))
data2 = as.data.frame(data2)
ggplot(data1, aes(x = "" , y = import_share, fill=fct_inorder(country))) +
labs(title = "U.S. Cattle Imports Country Shares - %")+
geom_col(width = 1, color = 1) +
coord_polar(theta = "y") +
scale_fill_brewer(palette = "Pastel1") +
geom_label_repel(data = data2,
aes(y = pos, label = paste0(import_share, "%")),
size = 4.5, nudge_x = 1, show.legend = FALSE) +
guides(fill = guide_legend(title = "Country")) +
theme_bw()+
plot_theme
Trade Balance
beef_trade_data = merge( export_data,import_data, by = c("country", "year"), all=T)
beef_trade_data = beef_trade_data[, trade_balance:= export-import]
beef_trade_data = beef_trade_data[, us_status:= ifelse(trade_balance<0, "net_importer", "net_exporter")]
beef_total_trade = beef_trade_data[country=="Total" ]
setorder(beef_total_trade, cols = -"year")
table_data = as.data.table(beef_total_trade)
table_data %>%
.[, country:= NULL]%>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 12: U.S. Beef Trade-1000 pounds')
), colnames = c('Year', 'Export', 'Import', 'Balance', 'Position'))%>%
formatCurrency(c('export','import', 'trade_balance'), currency = "", interval = 3, mark = ",", digits=0)3.3. Prices
Cattle
table_data = as.data.table(cattle_price_data)
table_data %>%
.[order(-date)]%>%
.[, date:=as.character(date)] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 13: U.S. Cattle Prices- Dollars per cwt')
), colnames = c('Date','Beef Cattle','Calves', 'Cows','Steers/Heifers'))%>%
formatCurrency(c('all_beef_cattle', 'calves', 'cows', 'steer_and_heifers'),currency = "", interval = 3, mark = ",", digits = 0)dt =
cattle_price_data %>%
as.data.frame(dt) %>%
select(date, all_beef_cattle, calves, cows, steer_and_heifers) %>%
gather(key = "price", value = "value", -date)
ggplot(dt, aes(x = date, y = value)) +
labs(title = "U.S. Cattle Prices-Dollars per cwt") +
geom_line(aes(color = price), size = 1) +
scale_color_manual(values = c("orange", "lightblue", "lightgreen", "black")) +
theme_bw()+
plot_theme
Beef
table_data = as.data.table(beef_price_data[, -c(5:7)])
table_data %>%
.[order(-date)]%>%
.[, date:=as.character(date)] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 13: U.S. Beef Prices- Cents per pound')
), colnames = c('Date', 'Farm','Wholesale','Retail'))%>%
formatCurrency(c('farm','wholesale','retail'),currency = "", interval = 3, mark = ",", digits = 0)dt =
beef_price_data[, -c(5:7), with=FALSE] %>%
as.data.frame(dt) %>%
select(date, farm, wholesale, retail) %>%
gather(key = "price", value = "value", -date)
ggplot(dt, aes(x = date, y = value)) +
labs(title = "U.S. Beef Prices-cents per pound") +
geom_line(aes(color = price), size = 1) +
scale_color_manual(values = c("orange", "lightblue", "lightgreen")) +
theme_bw()+
plot_theme
table_data = as.data.table(beef_price_data[, -c(2:4)])
table_data %>%
.[order(-date)]%>%
.[, date:=as.character(date)] %>%
datatable(table_data,caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: center;', ('Table 14: U.S. Beef Price Spreads- Cents per pound')
), colnames = c('Date', 'Farm-Retail','Farm-Wholesale','Wholesale-Retail'))%>%
formatCurrency(c('farm-retail','farm-wholesale','wholesale-retail'),currency = "", interval = 3, mark = ",", digits = 0)dt =
beef_price_data[, -c(2:4), with=FALSE] %>%
as.data.frame(dt) %>%
select(date, `farm-retail`, `farm-wholesale`, `wholesale-retail`) %>%
gather(key = "spread", value = "value", -date)
ggplot(dt, aes(x = date, y = value)) +
labs(title = "U.S. Beef Price Spreads") +
geom_line(aes(color = spread), size = 1) +
scale_color_manual(values = c("orange", "lightblue", "lightgreen")) +
theme_bw()+
plot_theme
3.4. Consumption
